I think model training is pretty hard to do efficiently on a vastly distributed network. If the model cant fit into the VRAM of the node your performance becomes so bad its useless, so a distributed model could only be properly trained if the size of the model doesnt exceed the majority of the nodes VRAM sizes. Maybe there is a different way of doing training but this would be the only way I can see. And it would still be much worse than just using a big datacenter where everything is fully interconnected. BOINC projects work great because its usually just a lot of small compute and memory required so every old desktop and laptop can contribute. Training a model which can compete and is not tiny requires neither low compute or low memory amount. BOINC tasks take minutes usually or sometimes hours but not weeks or months like training a model from scratch. But something like 7B or lower could maybe be trained like this. Im not sure but I think someone is already working on something like this but I dont remember the name of the project.
My understanding is that in addition to your comment and the development of a method to separate the training data for distributed learning, the latency/bandwidth of systems connected on the internet is a challenge, too. Information has to be sent around before and after any hypothetical number crunching.
This has been a (noble) goal of lots of different projects in the community for a long time. Federated learning projects like Flower have been chipping away at it for a long time. There are many many hurdles to be cleared before anything in this area is super feasible as an alternative, but I applaud everyone who works on it.